CI using t-theory
In previous courses, you have created confidence intervals with the formula of statistic plus/minus some number of standard errors. With bootstrapping, we typically use two standard errors. With t-based theory, we use the specific t-multiplier.
Create a CI for the slope parameter using both the default tidy() call as well as mutate() to calculate the confidence interval bounds explicitly. Note that the two methods should give exactly the same CI values because they are using the same computations.
alpha has been set to 0.05 and the degrees of freedom of the twins dataset is given to you.
Cet exercice fait partie du cours
Inference for Linear Regression in R
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
alpha <- 0.05
degrees_of_freedom <- nrow(twins) - 2
# Calculate the confidence level
confidence_level <- ___
# Calculate the upper percentile cutoff
p_upper <- ___
# Find the critical value from the t-distribution
critical_value <- ___